Espoo, Uusimaa, Suomi / Finland
Hybrid
The School of Engineering drives science and innovation in industrial and built environment technologies. We are committed to educating a new generation of experts who combine technical excellence with a deep understanding of sustainable development in shaping societies. Our research focuses on sustainable built environment, mechanics and materials, multidisciplinary energy technology, and design and implementation of technical systems. The strength of our school lies in close collaboration with stakeholders across research and education. About 45 doctoral candidates and 350 master’s students graduate from the school every year. The school is home to 700 staff members, including 70 professors. To learn more, please visit eng.aalto.fi.
School of Engineering is an international unit with nearly 70 talented professors and 3400 full-time students. Currently, the Department of Mechanical Engineering has 30 professors together with lecturers, post-doctoral researchers, doctoral students, and technical staff. In total, the number of personnel is about 300.
VIcTOR - Virtual Torque Sensing for Maritime Powertrains
Knowledge of the torque and torsional response of a maritime powertrain is important for emission-compliant control. Torque and torsional response are normally measured using strain gauges, dedicated torque sensors, or other instrumentation installed on the drivetrain. Torsional response can also be calculated if the external torsional forces acting on the powertrain are known, but measurements of this external forcing are rare in maritime powertrains. Instrumenting large maritime powertrains is also costly, and there is often not enough measured information available to fully determine the torsional response using conventional methods alone.
Unknown external forces and torsional response can instead be estimated using methods such as convex optimization or Kalman filtering. These torque estimation methods allow much more freedom in sensor placement and reduce the number of sensors needed to characterize the full torsional response of the powertrain.
This project aims to develop a torque estimation procedure that determines the unknown input torque and full torsional response using only encoder (speed, position) measurements together with selected available measurements from the driving internal combustion engine (e.g. cylinder pressure) or the driven electric generator (e.g. electromagnetic torque) — removing the need for an expensive, dedicated torque transducer.
Two complementary approaches will be explored. The first is convex optimization-based torque estimation, which allows constraints to be incorporated flexibly, both to keep estimated quantities physically consistent and to enforce desired structure in the results. The second is physics-informed learning, which combines available historical data with known system dynamics as a regulariser for data-driven learning — enabling nonlinear system dynamics to be learned indirectly with less training data than traditional data-driven approaches, for example to estimate the external torsional excitation from the propeller.
To ultimately remove the need for a torque flange, the estimation method must be integrated into the embedded engine control system. The work will make use of industry-standard tools such as Matlab Simulink to connect with existing control systems and support SIL/MIL simulation, and the resulting algorithm must run efficiently within the CPU constraints of an embedded system.
The project is carried out in collaboration with Wärtsilä, which provides powertrain data, operational torsional-dynamics measurements for model verification, and access to a physical maritime powertrain for validating the developed torque estimation procedure. You will be employed by the University, but your work will be conducted physically both at Aalto University campus and Wärtsilä Vaasa STH laboratory, to provide best-possible methodological support for your doctoral dissertation project.
Please note that these are considered assets, not strict requirements:
If you want to join our community, please submit your application through our recruitment system by November 11th 2026 at 23:59 Finnish time (UTC +2).
Please note: Aalto University's employees and visitors should apply for the position via our internal HR system Workday (Internal Jobs) using their existing Workday user account (not via the external webpage for open positions). Aalto University's students and visitors should apply as external candidates with a personal (not aalto.fi) email.
The position will be filled as soon as a suitable candidate is identified.
For additional information, please contact:
Assistant Professor Raine Viitala (raine.viitala@aalto.fi)
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